Three Main Goals of this File Produce Cleaner looking code. Identify the amount of clusters there are Identify the top genes expressed in each of the clusters
Save things as RDS file so I dont have to rerun the whole code
options(future.globals.maxSize = 74 * 1024^3) # 55 GB
getOption("future.globals.maxSize") #59055800320## [1] 79456894976
Based off this I can see that
SO1-> control SO2 -> low_salt SO3 -> low_salt SO4 -> control
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 11426
## Number of edges: 376311
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8730
## Number of communities: 6
## Elapsed time: 1 seconds
## Calculating cluster 0
## For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the presto package
## --------------------------------------------
## install.packages('devtools')
## devtools::install_github('immunogenomics/presto')
## --------------------------------------------
## After installation of presto, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per session
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
SO5m %>%
group_by(cluster) %>%
dplyr::filter(avg_log2FC > 1) %>%
slice_head(n = 5) %>%
ungroup() -> top10
DoHeatmap(SO5, features = top10$gene) + NoLegend()## Warning in DoHeatmap(SO5, features = top10$gene): The following features were
## omitted as they were not found in the scale.data slot for the SCT assay: Ifi47,
## Rpl3-ps1
Observation : MCUB seems to be the highly defined gene in low salt
## Cluster 3
# Subset Cluster 0, 1, 3
# SO6 checking another cluster
SO6<- subset(SO5, idents = c("0","1","3"))
SO6 <- FindNeighbors(SO6, dims = 1:30, verbose = F)
SO6 <- FindClusters(SO6, resolution = 0.1)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 8805
## Number of edges: 290708
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9087
## Number of communities: 2
## Elapsed time: 0 seconds
## Calculating cluster 0
## Calculating cluster 1
My guess is that these are the same, As you go from control to low_salt the cells start to express different genes. How can I test this?
I think the next step after I figure out something with these clusters is to figure out what each of these top genes do, the functions, and purpose of them.